explainiverse 0.13.2

Unified, extensible explainability framework supporting 23 XAI methods including LIME, SHAP, LRP, TCAV, GradCAM, HiResCAM, ScoreCAM, and more

Explainiverse

Explainiverse is a unified, extensible Python framework for Explainable AI (XAI). It provides a standardized interface for 23 state-of-the-art explanation methods across local, global, gradient-based, concept-based, and example-based paradigms, along with 55 evaluation metrics across 8 categories for assessing explanation quality — 49% more metrics than Quantus, the previous state of the art.

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Key Features

Feature	Description
23 Explainers	LIME, KernelSHAP, TreeSHAP, Integrated Gradients, DeepLIFT, DeepSHAP, SmoothGrad, Saliency Maps, GradCAM/GradCAM++, HiResCAM, XGradCAM, LayerCAM, EigenCAM, ScoreCAM, LRP, TCAV, Anchors, Counterfactual, Permutation Importance, PDP, ALE, SAGE, ProtoDash
55 Evaluation Metrics	Faithfulness (17), Robustness (7), Localisation (9), Fairness (6), Randomisation (5), Axiomatic (4), Stability (3), Complexity (3), Agreement (2) — see detailed tables below
Unified API	Consistent BaseExplainer interface with standardized Explanation output
Plugin Registry	Filter explainers by scope, model type, data type; automatic recommendations
Fairness Registry	Extensible FairnessMetricRegistry with decorator-based registration
Framework Support	Adapters for scikit-learn and PyTorch (with gradient computation)

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Explainer Coverage

Local Explainers (Instance-Level)

Method	Type	Reference
LIME	Perturbation	Ribeiro et al., 2016
KernelSHAP	Perturbation	Lundberg & Lee, 2017
TreeSHAP	Exact (Trees)	Lundberg et al., 2018
Integrated Gradients	Gradient	Sundararajan et al., 2017
DeepLIFT	Gradient	Shrikumar et al., 2017
DeepSHAP	Gradient + Shapley	Lundberg & Lee, 2017
SmoothGrad	Gradient	Smilkov et al., 2017
Saliency Maps	Gradient	Simonyan et al., 2014
GradCAM / GradCAM++	Gradient (CNN)	Selvaraju et al., 2017
HiResCAM	Gradient (CNN)	Draelos & Carin, 2020
XGradCAM	Gradient (CNN)	Fu et al., 2020
LayerCAM	Gradient (CNN)	Jiang et al., 2021
EigenCAM	Activation (CNN)	Muhammad & Yeasin, 2020
ScoreCAM	Perturbation (CNN)	Wang et al., 2020
LRP	Decomposition	Bach et al., 2015
TCAV	Concept-Based	Kim et al., 2018
Anchors	Rule-Based	Ribeiro et al., 2018
Counterfactual	Contrastive	Mothilal et al., 2020
ProtoDash	Example-Based	Gurumoorthy et al., 2019

Global Explainers (Model-Level)

Method	Type	Reference
Permutation Importance	Feature Importance	Breiman, 2001
Partial Dependence (PDP)	Feature Effect	Friedman, 2001
ALE	Feature Effect	Apley & Zhu, 2020
SAGE	Shapley Importance	Covert et al., 2020

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Evaluation Metrics (55 total)

Explainiverse provides the most comprehensive evaluation metrics suite among XAI frameworks, with 55 metrics across 8 categories — 49% more than Quantus (37 metrics).

Faithfulness (17 metrics)

Metric	Description	Reference
PGI	Prediction Gap on Important features	Petsiuk et al., 2018
PGU	Prediction Gap on Unimportant features	Petsiuk et al., 2018
Comprehensiveness	Drop when removing top-k features	DeYoung et al., 2020
Sufficiency	Prediction using only top-k features	DeYoung et al., 2020
Faithfulness Correlation	Correlation between attribution and impact	Bhatt et al., 2020
Faithfulness Estimate	Correlation of attributions with single-feature perturbation impact	Alvarez-Melis & Jaakkola, 2018
Monotonicity	Sequential feature addition shows monotonic prediction increase	Arya et al., 2019
Monotonicity-Nguyen	Spearman correlation between attributions and feature removal impact	Nguyen & Martinez, 2020
Pixel Flipping	AUC of prediction degradation when removing features by importance	Bach et al., 2015
Region Perturbation	AUC of prediction degradation when perturbing feature regions	Samek et al., 2015
Selectivity (AOPC)	Average prediction drop when sequentially removing features	Montavon et al., 2018
Sensitivity-n	Correlation between attribution sums and prediction changes for subsets	Ancona et al., 2018
IROF	Iterative Removal of Features — area over prediction degradation curve	Rieger & Hansen, 2020
Infidelity	How well attributions predict model output changes under perturbation	Yeh et al., 2019
ROAD	RemOve And Debias — noisy linear imputation for OOD-robust evaluation	Rong et al., 2022
Insertion AUC	AUC of prediction recovery when inserting features by importance	Petsiuk et al., 2018
Deletion AUC	AUC of prediction degradation when deleting features by importance	Petsiuk et al., 2018

Robustness (7 metrics)

Metric	Description	Reference
Max-Sensitivity	Maximum explanation change under input perturbation	Yeh et al., 2019
Avg-Sensitivity	Average explanation change under input perturbation	Yeh et al., 2019
Continuity	Lipschitz-based smoothness of explanation function	Montavon et al., 2018
Consistency	Agreement of explanations across similar inputs with same prediction	Dasgupta et al., 2022
Relative Input Stability (RIS)	Normalized explanation change relative to input change	Agarwal et al., 2022
Relative Representation Stability (RRS)	Normalized explanation change relative to representation change	Agarwal et al., 2022
Relative Output Stability (ROS)	Normalized explanation change relative to output change	Agarwal et al., 2022

Localisation (9 metrics)

Metric	Description	Reference
Pointing Game	Whether max attribution falls within ground-truth region	Zhang et al., 2018
Attribution Localisation	Fraction of positive attributions inside the ground-truth region	Kohlbrenner et al., 2020
Top-K Intersection	Overlap between top-k attributed features and ground-truth features	Theiner et al., 2021
Relevance Mass Accuracy	Mass of attribution within the ground-truth region	Arras et al., 2022
Relevance Rank Accuracy	Rank accuracy of attribution within the ground-truth region	Arras et al., 2022
AUC	ROC-AUC treating attribution as classifier for ground-truth mask	Fawcett, 2006
Energy-Based Pointing Game	Ratio of attribution energy inside vs total	Wang et al., 2020
Focus	Concentration of attribution mass in relevant regions	Arias-Duart et al., 2022
Attribution IoU	Intersection-over-Union between thresholded attribution and mask	—

Fairness (6 metrics)

Metric	Description	Reference
Group Fairness	Disparity of explanation quality across demographic groups	Dai et al., 2022
Individual Fairness	Similar individuals should receive similar explanations	Dwork et al., 2012
Counterfactual Explanation Fairness	Fairness of counterfactual explanations across protected groups	Kusner et al., 2017
Fidelity Disparity	Disparity in explanation fidelity across demographic groups	Balagopalan et al., 2022
Attribution Parity	Equal distribution of feature attributions across groups	Avodji et al., 2019
Conditional Fairness	Fairness of explanations conditioned on legitimate features	Hardt et al., 2016

Randomisation (5 metrics)

Metric	Description	Reference
MPRT	Model Parameter Randomisation Test — sanity check for saliency	Adebayo et al., 2018
Random Logit Test	Explanation sensitivity to output logit randomisation	Sixt et al., 2020
Smooth MPRT	Smoothed variant of MPRT for reduced variance	Hedström et al., 2023
Efficient MPRT	Computationally efficient MPRT approximation	Hedström et al., 2023
Data Randomisation Test	Explanation sensitivity to training label randomisation	Adebayo et al., 2018

Axiomatic (4 metrics)

Metric	Description	Reference
Completeness	Attributions sum to difference between output and baseline	Sundararajan et al., 2017
Non-Sensitivity	Zero attribution for features that do not influence the output	Nguyen & Martinez, 2020
Input Invariance	Attributions invariant to input transformations that preserve output	Kindermans et al., 2017
Symmetry	Symmetric features receive equal attributions	Sundararajan et al., 2017

Stability (3 metrics)

Metric	Description	Reference
RIS	Relative Input Stability (simplified)	Agarwal et al., 2022
ROS	Relative Output Stability (simplified)	Agarwal et al., 2022
Lipschitz Estimate	Local Lipschitz continuity of explanation function	Alvarez-Melis & Jaakkola, 2018

Complexity (3 metrics)

Metric	Description	Reference
Sparseness	Gini coefficient of attribution distribution	Chalasani et al., 2020
Complexity	Entropy-based complexity of the attribution	Bhatt et al., 2020
Effective Complexity	Number of features with attribution above threshold	Nguyen & Martinez, 2020

Agreement (2 metrics)

Metric	Description	Reference
Feature Agreement	Overlap in top-k features between two explanation methods	Krishna et al., 2022
Rank Agreement	Rank correlation between two explanation methods	Krishna et al., 2022

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Installation

# From PyPI
pip install explainiverse

# With PyTorch support (for gradient-based methods)
pip install explainiverse[torch]

# For development
git clone https://github.com/jemsbhai/explainiverse.git
cd explainiverse
poetry install

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Quick Start

Basic Usage with Registry

from explainiverse import default_registry, SklearnAdapter
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris

# Train a model
iris = load_iris()
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(iris.data, iris.target)

# Wrap with adapter
adapter = SklearnAdapter(model, class_names=iris.target_names.tolist())

# List all available explainers
print(default_registry.list_explainers())
# ['lime', 'shap', 'treeshap', 'integrated_gradients', 'deeplift', 'deepshap',
#  'smoothgrad', 'saliency', 'gradcam', 'lrp', 'tcav', 'anchors', 'counterfactual',
#  'protodash', 'permutation_importance', 'partial_dependence', 'ale', 'sage']

# Create an explainer via registry
explainer = default_registry.create(
    "lime",
    model=adapter,
    training_data=iris.data,
    feature_names=iris.feature_names.tolist(),
    class_names=iris.target_names.tolist()
)

# Generate explanation
explanation = explainer.explain(iris.data[0])
print(explanation.explanation_data["feature_attributions"])

Filter and Recommend Explainers

# Filter by criteria
local_explainers = default_registry.filter(scope="local", data_type="tabular")
neural_explainers = default_registry.filter(model_type="neural")
image_explainers = default_registry.filter(data_type="image")

# Get recommendations
recommendations = default_registry.recommend(
    model_type="neural",
    data_type="tabular",
    scope_preference="local",
    max_results=5
)

---

Gradient-Based Explainers (PyTorch)

Integrated Gradients

from explainiverse import PyTorchAdapter
from explainiverse.explainers.gradient import IntegratedGradientsExplainer
import torch.nn as nn

# Define and wrap model
model = nn.Sequential(
    nn.Linear(10, 64), nn.ReLU(),
    nn.Linear(64, 32), nn.ReLU(),
    nn.Linear(32, 3)
)
adapter = PyTorchAdapter(model, task="classification", class_names=["A", "B", "C"])

# Create explainer
explainer = IntegratedGradientsExplainer(
    model=adapter,
    feature_names=[f"feature_{i}" for i in range(10)],
    class_names=["A", "B", "C"],
    n_steps=50,
    method="riemann_trapezoid"
)

# Explain with convergence check
explanation = explainer.explain(X[0], return_convergence_delta=True)
print(f"Attributions: {explanation.explanation_data['feature_attributions']}")
print(f"Convergence delta: {explanation.explanation_data['convergence_delta']:.6f}")

Layer-wise Relevance Propagation (LRP)

from explainiverse.explainers.gradient import LRPExplainer

# LRP - Decomposition-based attribution with conservation property
explainer = LRPExplainer(
    model=adapter,
    feature_names=feature_names,
    class_names=class_names,
    rule="epsilon",       # Propagation rule: epsilon, gamma, alpha_beta, z_plus, composite
    epsilon=1e-6          # Stabilization constant
)

# Basic explanation
explanation = explainer.explain(X[0], target_class=0)
print(explanation.explanation_data["feature_attributions"])

# Verify conservation property (sum of attributions ~ target output)
explanation = explainer.explain(X[0], return_convergence_delta=True)
print(f"Conservation delta: {explanation.explanation_data['convergence_delta']:.6f}")

# Compare different LRP rules
comparison = explainer.compare_rules(X[0], rules=["epsilon", "gamma", "z_plus"])
for rule, result in comparison.items():
    print(f"{rule}: top feature = {result['top_feature']}")

# Layer-wise relevance analysis
layer_result = explainer.explain_with_layer_relevances(X[0])
for layer, relevances in layer_result["layer_relevances"].items():
    print(f"{layer}: sum = {sum(relevances):.4f}")

# Composite rules: different rules for different layers
explainer_composite = LRPExplainer(
    model=adapter,
    feature_names=feature_names,
    class_names=class_names,
    rule="composite"
)
explainer_composite.set_composite_rule({
    0: "z_plus",    # Input layer: focus on what's present
    2: "epsilon",   # Middle layers: balanced
    4: "epsilon"    # Output layer
})
explanation = explainer_composite.explain(X[0])

LRP Propagation Rules:

Rule	Description	Use Case
epsilon	Adds stabilization constant	General purpose (default)
gamma	Enhances positive contributions	Image classification
alpha_beta	Separates pos/neg (alpha-beta=1)	Fine-grained control
z_plus	Only positive weights	Input layers, what's present
composite	Different rules per layer	Best practice for deep nets

DeepLIFT and DeepSHAP

from explainiverse.explainers.gradient import DeepLIFTExplainer, DeepLIFTShapExplainer

# DeepLIFT - Fast reference-based attributions
deeplift = DeepLIFTExplainer(
    model=adapter,
    feature_names=feature_names,
    class_names=class_names,
    baseline=None  # Uses zero baseline by default
)
explanation = deeplift.explain(X[0])

# DeepSHAP - DeepLIFT averaged over background samples
deepshap = DeepLIFTShapExplainer(
    model=adapter,
    feature_names=feature_names,
    class_names=class_names,
    background_data=X_train[:100]
)
explanation = deepshap.explain(X[0])

SmoothGrad

from explainiverse.explainers.gradient import SmoothGradExplainer

# SmoothGrad - Noise-averaged gradients for smoother saliency
explainer = SmoothGradExplainer(
    model=adapter,
    feature_names=feature_names,
    class_names=class_names,
    n_samples=50,
    noise_scale=0.15,
    noise_type="gaussian"  # or "uniform"
)

# Standard SmoothGrad
explanation = explainer.explain(X[0], method="smoothgrad")

# SmoothGrad-Squared (sharper attributions)
explanation = explainer.explain(X[0], method="smoothgrad_squared")

# VarGrad (variance of gradients)
explanation = explainer.explain(X[0], method="vargrad")

GradCAM for CNNs

from explainiverse.explainers.gradient import GradCAMExplainer

# For CNN models
adapter = PyTorchAdapter(cnn_model, task="classification", class_names=class_names)

explainer = GradCAMExplainer(
    model=adapter,
    target_layer="layer4",  # Last conv layer
    class_names=class_names,
    method="gradcam++"  # or "gradcam"
)

explanation = explainer.explain(image)
heatmap = explanation.explanation_data["heatmap"]

---

Evaluation Examples

Faithfulness

from explainiverse.evaluation import (
    compute_pgi, compute_pgu,
    compute_comprehensiveness, compute_sufficiency,
    compute_faithfulness_correlation
)

pgi = compute_pgi(model=adapter, instance=X[0], attributions=attributions,
                  feature_names=feature_names, top_k=3)

comp = compute_comprehensiveness(model=adapter, instance=X[0], attributions=attributions,
                                 feature_names=feature_names, top_k_values=[1, 2, 3, 5])

Robustness

from explainiverse.evaluation import (
    compute_max_sensitivity, compute_avg_sensitivity,
    compute_continuity, compute_consistency,
    compute_relative_input_stability
)

max_sens = compute_max_sensitivity(
    explainer=explainer, instance=X[0],
    n_perturbations=10, perturbation_scale=0.1
)

Localisation

from explainiverse.evaluation import (
    LocalisationMask, compute_pointing_game,
    compute_attribution_localisation, compute_attribution_iou
)

mask = LocalisationMask(mask=binary_mask, feature_names=feature_names)
pg = compute_pointing_game(attributions=attributions, mask=mask)
iou = compute_attribution_iou(attributions=attributions, mask=mask, threshold=0.5)

Fairness

from explainiverse.evaluation import (
    compute_group_fairness, compute_individual_fairness,
    compute_counterfactual_fairness, compute_fidelity_disparity,
    compute_attribution_parity, compute_conditional_fairness
)

gf = compute_group_fairness(
    attributions_list=attributions_list,
    sensitive_features=group_labels,
    inner_metric=None  # defaults to L1 norm
)

ind_f = compute_individual_fairness(
    attributions_list=attributions_list,
    instances=X, distance_threshold=0.5
)

Randomisation

from explainiverse.evaluation import compute_mprt, compute_random_logit

mprt = compute_mprt(model=pytorch_model, explainer=explainer,
                    instance=X[0], target_class=0)

rlt = compute_random_logit(model=pytorch_model, explainer=explainer,
                           instance=X[0], target_class=0)

Axiomatic

from explainiverse.evaluation import (
    compute_completeness, compute_non_sensitivity,
    compute_input_invariance, compute_symmetry
)

comp = compute_completeness(model=adapter, instance=X[0],
                            attributions=attributions, baseline=baseline)

sym = compute_symmetry(model=adapter, instance=X[0],
                       attributions=attributions,
                       symmetric_indices=[(0, 1)])

---

Global Explainers

from explainiverse.explainers import (
    PermutationImportanceExplainer,
    PartialDependenceExplainer,
    ALEExplainer,
    SAGEExplainer
)

# Permutation Importance
perm_imp = PermutationImportanceExplainer(
    model=adapter, X=X_test, y=y_test,
    feature_names=feature_names, n_repeats=10
)
explanation = perm_imp.explain()

# ALE (handles correlated features)
ale = ALEExplainer(model=adapter, X=X_train, feature_names=feature_names)
explanation = ale.explain(feature="feature_0", n_bins=20)

# SAGE (global Shapley importance)
sage = SAGEExplainer(
    model=adapter, X=X_train, y=y_train,
    feature_names=feature_names, n_permutations=512
)
explanation = sage.explain()

---

Multi-Explainer Comparison

from explainiverse import ExplanationSuite

suite = ExplanationSuite(
    model=adapter,
    explainer_configs=[
        ("lime", {"training_data": X_train, "feature_names": feature_names, "class_names": class_names}),
        ("shap", {"background_data": X_train[:50], "feature_names": feature_names, "class_names": class_names}),
        ("treeshap", {"feature_names": feature_names, "class_names": class_names}),
    ]
)

results = suite.run(X_test[0])
suite.compare()

---

Custom Explainer Registration

Explainiverse's plugin architecture allows you to register custom explainers that integrate seamlessly with the registry's discovery, filtering, and recommendation system.

from explainiverse import default_registry, BaseExplainer, Explanation
from explainiverse.core.registry import ExplainerMeta

@default_registry.register_decorator(
    name="my_explainer",
    meta=ExplainerMeta(
        scope="local",
        model_types=["any"],
        data_types=["tabular"],
        task_types=["classification", "regression"],
        description="My custom attribution method",
        paper_reference="Author et al., 2024 - 'My Method' (Conference)",
        complexity="O(n * d)",
        requires_training_data=False,
        supports_batching=True
    )
)
class MyExplainer(BaseExplainer):
    def __init__(self, model, feature_names, class_names=None, **kwargs):
        super().__init__(model)
        self.feature_names = feature_names
        self.class_names = class_names

    def explain(self, instance, target_class=None, **kwargs):
        attributions = self._compute_attributions(instance, target_class)
        return Explanation(
            explainer_name="MyExplainer",
            target_class=str(target_class or 0),
            explanation_data={"feature_attributions": attributions},
            feature_names=self.feature_names,
            metadata={"method": "my_method", "params": kwargs}
        )

---

Architecture

explainiverse/
├── core/
│   ├── explainer.py          # BaseExplainer abstract class
│   ├── explanation.py        # Unified Explanation container
│   └── registry.py           # ExplainerRegistry with metadata
├── adapters/
│   ├── sklearn_adapter.py    # scikit-learn models
│   └── pytorch_adapter.py    # PyTorch with gradient support
├── explainers/
│   ├── attribution/          # LIME, SHAP, TreeSHAP
│   ├── gradient/             # IG, DeepLIFT, DeepSHAP, SmoothGrad, Saliency, GradCAM, LRP, TCAV
│   │                         # + HiResCAM, XGradCAM, LayerCAM, EigenCAM, ScoreCAM
│   ├── rule_based/           # Anchors
│   ├── counterfactual/       # DiCE-style
│   ├── global_explainers/    # Permutation, PDP, ALE, SAGE
│   └── example_based/        # ProtoDash
├── evaluation/
│   ├── faithfulness.py       # Core faithfulness (PGI, PGU, Comprehensiveness, Sufficiency)
│   ├── faithfulness_extended.py  # 12 extended faithfulness metrics
│   ├── stability.py          # RIS, ROS, Lipschitz (simplified)
│   ├── robustness.py         # 7 robustness metrics (Phase 2)
│   ├── agreement.py          # Feature Agreement, Rank Agreement (Phase 2)
│   ├── complexity.py         # Sparseness, Complexity, Effective Complexity (Phase 4)
│   ├── localisation.py       # 9 localisation metrics (Phase 3)
│   ├── randomisation.py      # 5 randomisation metrics (Phase 5)
│   ├── axiomatic.py          # 4 axiomatic metrics (Phase 6)
│   ├── fairness.py           # 6 fairness metrics + FairnessMetricRegistry (Phase 7)
│   ├── metrics.py            # AOPC, ROAR
│   └── _utils.py             # Shared utilities
└── engine/
    └── suite.py              # Multi-explainer comparison

---

Running Tests

# Run all tests
poetry run pytest

# Run with coverage
poetry run pytest --cov=explainiverse --cov-report=html

# Run specific test file
poetry run pytest tests/test_fairness.py -v

# Run specific test class
poetry run pytest tests/test_lrp.py::TestLRPConv2d -v

---

Roadmap

Completed

• Core framework (BaseExplainer, Explanation, Registry)
• 18 explainers across 7 categories
• 55 evaluation metrics across 8 categories
• PyTorch adapter with gradient support
• FairnessMetricRegistry for extensible fairness evaluation
• Plugin system with decorator-based registration

Planned

• Attention-based explanations (for Transformers)
• TensorFlow/Keras adapter
• Interactive visualization dashboard
• Explanation caching and serialization
• Distributed computation support

---

Citation

If you use Explainiverse in your research, please cite:

@software{explainiverse2025,
  title = {Explainiverse: A Unified Framework for Explainable AI},
  author = {Syed, Muntaser},
  year = {2025},
  url = {https://github.com/jemsbhai/explainiverse},
  version = {0.13.0}
}

---

Contributing

Contributions are welcome! Please see our Contributing Guide for details.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Write tests for your changes
4. Ensure all tests pass (poetry run pytest)
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

---

License

MIT License - see LICENSE for details.

---

Acknowledgments

Explainiverse builds upon the foundational work of many researchers in the XAI community. We thank the authors of LIME, SHAP, Integrated Gradients, DeepLIFT, LRP, GradCAM, TCAV, Anchors, DiCE, ALE, SAGE, and ProtoDash for their contributions to interpretable machine learning.